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7 Must-Have Skills for Career in Machine Learning

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career in machine learning

Machine Learning is basically linked with Artificial Intelligence. This provides the computer systems with the ability to perform certain tasks that include recognition, diagnosis, planning, robot control, prediction etc. without being programmed clearly.

The process of Machine Learning is similar to that of Data Mining. Data is the major requirement for both the processes to look for the patterns. Still, instead of extracting data for the human conception as in the case of data mining applications, machine learning makes use of the data to enhance the program’s understanding. The programs of machine learning spot the outlines in the data and make an adjustment with the program actions accordingly.

Now, if you are trying to know about the skills that are essential to pursue a career in machine learning. A good candidate must have a deep and proper knowledge of the different sets of algorithms and applied math, skills like problem-solving and analytical thinking, and most importantly, the programming languages such as Python, C++, Java etc.

Here is a list of certain key skill sets.

  1. Python/C++/R/Java: It is true that if you want a job in Machine Learning, you will probably need to know all these languages. C++ can help in speeding up the code. In statistics and plots, R works very well and Hadoop is based on Java, so you may need to execute mappers and reducers in Java.
  2. Applied Math and Algorithms: By having a strong hold in understanding the algorithm theory and knowing about its working, you can easily discriminate models such as SVMs (Support-Vector Machines). Furthermore, you will need to understand the subjects like gradient descent, convex optimization, Lagrange, quadratic programming, partial differential equations and much more like these. You must also be aware of the summations.
  3. Probability and Statistics: You can easily understand the algorithms with the help of theories. Naïve Bayes, Gaussian Mixture Models, and Hidden Markov Models are some of the great samples. It is very important to have a strong knowledge of Probability and Stats to learn these models. Also, statistics can be used as a measure of model evaluation: receiver-operator curves, p-values, confusion matrices etc.
  4. Distributed Computing: In most of the cases, machine learning jobs involve the working of large sets of data these days. This data can’t be managed by using a single machine, hence, you will need to share it with the entire cluster. Projects like Apache Hadoop and cloud services like Amazon’s EC2 makes it easier and worthwhile.
  5. Learning about Advanced Signal Processing Techniques: Feature extraction is considered to be an important part of machine learning. Every problem is sorted with its particular solution. Hence, for that, you need to utilize cool and advance signal processing algorithms. These algorithms involve shearlets, wavelets, curvelets, bandlets. You must know about the time-frequency analysis and try to use it to solve your problems. The tree is signal processing 101 stuff, however.
  6. Expanding the Expertise in Unix Tools: It is important for you to master all the important Unix Tools that were specially designed for this: cat, grep, find, awk, sed, sort, cut, tr and many more. As all the processing is likely to be based on Linux-machine, it is important for you to access these tools. Not only this but learning their functions and utilizing them well is also important.
  7. Other Skills: (a) Update oneself- It is essential for you to stay up to date with all the changes and enhancements. It further means that you need to aware of the news regarding the development of the tools, theory, and algorithms. (b) Read a lot- Don’t miss to read out papers like Google Map-Reduce, Google File System, Google Big Table. You must also read the free machine learning books.

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